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Robust Representation for Data Analytics

Models and Applications

  • Sheng Li
  • Yun Fu

Part of the Advanced Information and Knowledge Processing book series (AI&KP)

Table of contents

  1. Front Matter
    Pages i-xi
  2. Sheng Li, Yun Fu
    Pages 1-5
  3. Robust Representation Models

    1. Front Matter
      Pages 7-7
    2. Sheng Li, Yun Fu
      Pages 9-16
    3. Sheng Li, Yun Fu
      Pages 17-44
    4. Sheng Li, Yun Fu
      Pages 45-71
    5. Sheng Li, Yun Fu
      Pages 73-93
    6. Sheng Li, Yun Fu
      Pages 95-119
  4. Applications

    1. Front Matter
      Pages 121-121
    2. Sheng Li, Yun Fu
      Pages 147-174
    3. Sheng Li, Yun Fu
      Pages 175-201
  5. Back Matter
    Pages 223-224

About this book

Introduction

This book introduces the concepts and models of robust representation learning, and provides a set of solutions to deal with real-world data analytics tasks, such as clustering, classification, time series modeling, outlier detection, collaborative filtering, community detection, etc. Three types of robust feature representations are developed, which extend the understanding of graph, subspace, and dictionary.

Leveraging the theory of low-rank and sparse modeling, the authors develop robust feature representations under various learning paradigms, including unsupervised learning, supervised learning, semi-supervised learning, multi-view learning, transfer learning, and deep learning. Robust Representations for Data Analytics covers a wide range of applications in the research fields of big data, human-centered computing, pattern recognition, digital marketing, web mining, and computer vision.

Keywords

Robust Representations Graph Construction Subspace Learning Outlier Detection Multi-view Learning

Authors and affiliations

  • Sheng Li
    • 1
  • Yun Fu
    • 2
  1. 1.Northeastern UniversityBostonUSA
  2. 2.Northeastern University BOSTONUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-60176-2
  • Copyright Information Springer International Publishing AG 2017
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-60175-5
  • Online ISBN 978-3-319-60176-2
  • Series Print ISSN 1610-3947
  • Series Online ISSN 2197-8441
  • Buy this book on publisher's site